Our work on self-organizing multi-agent systems lies at the intersection of Computer Science/AI, Robotics, and Biology. The main theme in our lab is understanding and engineering collective behavior, but we do it in many ways -- both theory and hardware, and both bio-inspired and collaborations with biologists. You can read more about the broad themes and projects in our lab on this page, see movies and talks of our work on our youtube channel, or read selected articles on our publications page.
Favorite Quote: "Building 1,000 robots is hard", McLurkin said. "Getting 1,000 robots to work together reliably is, how they’d say it in Boston? 'Wicked hard'."
We work on three main areas:
Bio-inspired Multi-agent Theory
We explore artificial multi-agent models inspired by self-organising and self-repairing behavior in biology. We are especially interested in global-to-local compilation and theory, i.e. how user-specified global goals can be translated into local agent interactions and how one can reason about the correctness and complexity of agent rules. Our goal is to show how biological design principles can be formally captured, generalized to new tasks, and theoretically analyzed.
Bio-inspired Robot Swarms
We study bio-inspired approaches for designing and programming robotic systems that rely on large numbers of relatively cheap and simple agents, e.g. reconfigurable modular robots, robot swarms (Termes, Kilobots, Robobees) and sensor networks. We are especially interested in the design and analysis of algorithms for decentralized coordination and the physical design (aka embodied intelligence) of autonomous robot collectives.
We develop mathematical and computational models of individual behavior to investigate how system-level properties emerge in collective systems. We work closely with experimental biologists. Our previous work focused on epithelial tissues in fruit fly development, relating local cell programs to global tissue-level outcomes. Our current work focuses on how social insects, such as mound-building termites and collectively-transporting ants, coordinate to achieve complex tasks.
collective intelligence, swarm intelligence, multi-agent systems,
self-organization, amorphous computing, global-to-local programming
bio-inspired robots, swarm robotics, modular self-adapting robots, sensor networks
decentralized algorithms, distributed computing, stigmergy, implicit coordination
systems biology, social insects, multicellular systems